1. Field of the Invention
The present invention relates to image processing technology, and more particularly, to an apparatus and method for more accurately extracting edge portions from an images in various conditions.
2. Description of the Related Art
The technology of extracting edges from an image is being applied in various image processing related fields. For example, in vision related fields that require edge extraction, general cameras are set up such that when taking photographs, they determine the presence of a person's face by extracting edges and are automatically focused on the person's face. Traffic surveillance systems, which require vehicle extraction from an image, vehicle tracking and recognition, and license plate extraction and recognition, also use edge information extracted by the edge extraction technology. Furthermore, edge extraction technology is essentially used in surveillance cameras, radiology, and other object recognition systems.
An edge is a point at which image brightness changes sharply from a low value to a high value or, conversely, from a high value to a low value. A sharp brightness change at the boundary of an object results in a higher slope value, and a gradual brightness change results in a lower slope value. As a way to extract this brightness change rate, that is, a slope, a differential operation is used. A slope in an image is referred to as a gradient. When the norm (size) of the gradient is greater than a threshold value, the pixel of interest is determined as belonging to an edge portion.
However, because edge extraction techniques based on a differential operator rely on the differential value between pixels for edge extraction, these techniques are insensitive to edges at which brightness changes gently, thus making effective edge extraction difficult. In addition, edge extraction involves an image division process using a threshold value. Here, an inappropriate threshold value directly affects the result of edge extraction. When a low threshold value is used, a lot of edge information can be obtained as a result of edge extraction but it causes an extraction of thick edge lines and high sensitivity to noise. On the other hand, when a high threshold value is used, not all edges can be extracted, resulting in a significant loss of edge information.
Therefore, various methods are being used to determine a threshold value. However, it is a very difficult challenge to find an appropriate threshold value. Using one fixed threshold value may be effective for some areas of an image, but in many cases, not for the entire image. This is because the brightness of the image is not uniform across all areas of the image. Wrongly extracted edges may later adversely affect image recognition performance.
Meanwhile, a method of determining a threshold value according to the condition of an image is available. In this method, a threshold value is determined using a representative value (mean and/or standard deviation) of brightness of an image. Thus, edges can be adaptively extracted from an image that changes over time. However, this method is effective for adaptive edge extraction in regard to time change but ineffective for adaptive edge extraction in regard to various areas of the image. For example, when a threshold value is determined using the mean of brightness of an image, if the brightness of the image is, on the whole, uniform across all areas of the image, appropriate edge extraction is possible. However, if the image has various brightness distributions in its areas, this method is ineffective. That is, using one fixed threshold value for one image may be effective for some areas of the image but ineffective for the entire image since the brightness of the image is not uniform across all areas of the image.
Therefore, a technology, which can adaptively extract an edge according to each brightness area of an image, is required for more effective edge extraction.